Job Description
Overview
PepsiCo is one of the world's leading food and beverage companies with more than $7+ Billion in Net Revenue and a global portfolio of diverse and beloved brands. We have a complementary food and beverage portfolio that includes 22 brands that each generate more than $1 Billion in annual retail sales. PepsiCo's products are sold in more than 200 countries and territories around the world. PepsiCo's strength is its people. We are over 250,000 game changers, mountain movers and history makers, located around the world, and united by a shared set of values and goals.
We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this Winning with Purpose. For more information on PepsiCo and the opportunities it holds, visit www.pepsico.com.
The Data Science Executive Manager will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Azure Machine Learning Services and Pipelines.
Responsibilities
• Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
• Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
• Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
• Use big data technologies to help process data and build scaled data pipelines (batch to real time)
• Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines leveraging the NGAA platform (Azure)
• Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
• Automate ML models deployments
Qualifications
Qualifications:
• 10+ years of overall experience that includes at least 6+ years of hands-on work experience data science /Machine learning
• Minimum 6+ year of SQL experience
• Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers (Azure preferred) is preferred
• BE/BS in Computer Science, Math, Physics, or other technical fields.
Skills, Abilities, Knowledge:
• Data Science – Hands on experience and strong knowledge of building machine learning models – supervised and unsupervised models
• Programming Skills – Hands-on experience in statistical programming languages like Python, R and database query languages like SQL
• Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators
• Cloud (Azure) – Experience in Databricks and ADF is desirable
• Familiarity with Spark, Hive, Pig is an added advantage
• Model deployment experience will be a plus
• Experience with version control systems like GitHub and CI/CD tools
• Experience is Exploratory data Analysis
• Knowledge of ML Ops / DevOps and deploying ML models is required
• Experience using MLFlow, Kubeflow etc. will be preferred
• Experience executing and contributing to ML OPS automation infrastructure is good to have
• Exceptional analytical and problem-solving skills
Differentiating Competencies Required
• Ability to work with virtual teams (remote work locations); lead team of technical resources (employees and contractors) based in multiple locations across geographies
• Lead technical discussions, driving clarity of complex issues/requirements to build robust solutions
• Strong communication skills to meet with business, understand sometimes ambiguous, needs, and translate to clear, aligned requirements
• Able to work independently with business partners to understand requirements quickly, perform analysis and lead the design review sessions
• Highly influential and having the ability to educate challenging stakeholders on the role of data and its purpose in the business
• Places the user in the centre of decision making
• Teams up and collaborates for speed, agility, and innovation
• Experience with and embraces agile methodologies
• Strong negotiation and decision-making skill
• Experience managing and working with globally distributed teams
PepsiCo is one of the world's leading food and beverage companies with more than $7+ Billion in Net Revenue and a global portfolio of diverse and beloved brands. We have a complementary food and beverage portfolio that includes 22 brands that each generate more than $1 Billion in annual retail sales. PepsiCo's products are sold in more than 200 countries and territories around the world. PepsiCo's strength is its people. We are over 250,000 game changers, mountain movers and history makers, located around the world, and united by a shared set of values and goals.
We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this Winning with Purpose. For more information on PepsiCo and the opportunities it holds, visit www.pepsico.com.
The Data Science Executive Manager will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Azure Machine Learning Services and Pipelines.
Responsibilities
• Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope
• Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities
• Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards
• Use big data technologies to help process data and build scaled data pipelines (batch to real time)
• Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure Pipelines leveraging the NGAA platform (Azure)
• Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure
• Automate ML models deployments
Qualifications
Qualifications:
• 10+ years of overall experience that includes at least 6+ years of hands-on work experience data science /Machine learning
• Minimum 6+ year of SQL experience
• Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers (Azure preferred) is preferred
• BE/BS in Computer Science, Math, Physics, or other technical fields.
Skills, Abilities, Knowledge:
• Data Science – Hands on experience and strong knowledge of building machine learning models – supervised and unsupervised models
• Programming Skills – Hands-on experience in statistical programming languages like Python, R and database query languages like SQL
• Statistics – Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators
• Cloud (Azure) – Experience in Databricks and ADF is desirable
• Familiarity with Spark, Hive, Pig is an added advantage
• Model deployment experience will be a plus
• Experience with version control systems like GitHub and CI/CD tools
• Experience is Exploratory data Analysis
• Knowledge of ML Ops / DevOps and deploying ML models is required
• Experience using MLFlow, Kubeflow etc. will be preferred
• Experience executing and contributing to ML OPS automation infrastructure is good to have
• Exceptional analytical and problem-solving skills
Differentiating Competencies Required
• Ability to work with virtual teams (remote work locations); lead team of technical resources (employees and contractors) based in multiple locations across geographies
• Lead technical discussions, driving clarity of complex issues/requirements to build robust solutions
• Strong communication skills to meet with business, understand sometimes ambiguous, needs, and translate to clear, aligned requirements
• Able to work independently with business partners to understand requirements quickly, perform analysis and lead the design review sessions
• Highly influential and having the ability to educate challenging stakeholders on the role of data and its purpose in the business
• Places the user in the centre of decision making
• Teams up and collaborates for speed, agility, and innovation
• Experience with and embraces agile methodologies
• Strong negotiation and decision-making skill
• Experience managing and working with globally distributed teams
💡 Quick Summary
Seeking a career-building opportunity? The Data Science Assoc Manager position is now open for candidates interested in the Database Administrator sector. This role in Gurugram offers a professional environment and growth potential.
Requirement Snapshot: Candidates should possess basic communication skills, a proactive attitude, and the ability to work in a team. Experience in Database Administrator is a plus.
